Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria

• Published in: International journal of environmental research and public health Vol. 22; no. 1; p. 47
• Main Authors: Rubio Maturana, Carles, de Oliveira, Allisson Dantas, Zarzuela, Francesc, Mediavilla, Alejandro, Martínez-Vallejo, Patricia, Silgado, Aroa, Goterris, Lidia, Muixí, Marc, Abelló, Alberto, Veiga, Anna, López-Codina, Daniel, Sulleiro, Elena, Sayrol, Elisa, Joseph-Munné, Joan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.12.2024; MDPI
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Brief Report; Humans; Image Processing, Computer-Assisted - methods; Infections; Laboratories; Leukocytes; Malaria; Malaria - diagnosis; Malaria - parasitology; Microscopy; Microscopy - economics; Microscopy - instrumentation; Microscopy - methods; Neural networks; Parasites; Parasitic diseases; Plasmodium - isolation & purification; Robotics; Sample size; Sensitivity and Specificity; Smartphones; Software; Spain; Statistical analysis; Tropical diseases; Spain; Sub-Saharan Africa; Plasmodium; infectious diseases; automated diagnosis; malaria; tropical medicine; point-of-care; artificial intelligence
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph22010047

The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify Plasmodium parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital image analysis were postulated as a suitable supportive alternative for automated malaria diagnosis. A diagnostic evaluation of the iMAGING AI-based system was conducted in the reference laboratory of the International Health Unit Drassanes-Vall d’Hebron in Barcelona, Spain. iMAGING is an automated device for the diagnosis of malaria by using artificial intelligence image analysis tools and a robotized microscope. A total of 54 Giemsa-stained thick blood smear samples from travelers and migrants coming from endemic areas were employed and analyzed to determine the presence/absence of Plasmodium parasites. AI diagnostic results were compared with expert light microscopy gold standard method results. The AI system shows 81.25% sensitivity and 92.11% specificity when compared with the conventional light microscopy gold standard method. Overall, 48/54 (88.89%) samples were correctly identified [13/16 (81.25%) as positives and 35/38 (92.11%) as negatives]. The mean time of the AI system to determine a positive malaria diagnosis was 3 min and 48 s, with an average of 7.38 FoV analyzed per sample. Statistical analyses showed the Kappa Index = 0.721, demonstrating a satisfactory correlation between the gold standard diagnostic method and iMAGING results. The AI system demonstrated reliable results for malaria diagnosis in a reference laboratory in Barcelona. Validation in malaria-endemic regions will be the next step to evaluate its potential in resource-poor settings.